CBMM, NSF STC » Finding Friend and Foe in Multi-Agent Games [video]
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Co-authors Max Kleiman-Weiner and Jack Serrino discuss their latest publication where they created a new algorithm. The algorithm that the team developed, dubbed DeepRole, has three components. First, it plays against itself, iteratively, with variation, boosting the likelihood of actions that are beneficial to each player, improving slowly. This has worked well in other games, such as Go and poker. Second, it implements deductive reasoning, based on observed actions, and tracks the belief about the assignment of roles (spy or non-spy) given the history of actions. This inference, a computational mechanism to distinguish friend and foe from actions, is a type of Theory of Mind. It was Serrino who, as an Avalon enthusiast, brought detailed insight into how people actually play the game, which helped build the inferential computation. The space of Avalon actions is too large for brute-force search, so in the third element of the algorithm (as in successful poker AI) a deep neural network is used to prune the search tree.